Development of a web-based insulin decision aid for the elderly: usability barriers and guidelines

Universal Access in the Information Society - In recent years, researchers have attempted to shift patient decision aids (PDAs) from paper-based to web-based to increase its accessibility. Insulin...     

Customer Retention via Data Mining

``Customer Retention' is an increasingly pressing issue intoday's ever-competitive commercial arena. This is especially relevantand important for sales and services related industries. Motivated by areal-world problem faced by a large company, we proposed a solution thatintegrates various techniques of data mining, such as featureselection via induction, deviation analysis, and mining multipleconcept-level association rules to form an intuitive and novel approachto gauging customer loyalty and predicting their likelihood ofdefection. Immediate action triggered by these ``early-warnings'resulting from data mining is often the key to eventual customerretention.     

Identification and sizing of the entirely visible rocks from a 3D surface data segmentation of laboratory rock piles

Once segmentation of 3D surface data of a rock pile has been performed, the next task is to determine the visibility of the surface rocks. A region boundary-following algorithm that accommodates irregularly spaced 3D coordinate data is presented for determining this visibility. We examine 3D surface segmentations of laboratory rock piles and determine which regions in the segmentation correspond to entirely visible rocks, and which correspond to overlapped or partially visible rocks. This is a significant distinction as it allows accurate size determination of entirely visible rocks, separate handling of partially visible rocks, and prevents erroneous bias resulting from mishandling partially visible rocks as smaller entirely visible rocks. Literature review indicates that other rock pile sizing techniques fail to make this distinction. The rock visibility results are quantified by comparison to manual surface classifications of the laboratory piles and the size results are quantified by comparison to the sieve size.     

A novel generic hebbian ordering-based fuzzy rule base reduction approach to mamdani neuro-fuzzy system

There are two important issues in neuro-fuzzy modeling: (1) interpretability--the ability to describe the behavior of the system in an interpretable way--and (2) accuracy--the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.     

Single machine batch scheduling problem with family setup times and release dates to minimize makespan

In this paper we consider the single machine batch scheduling problem with family setup times and release dates to minimize makespan. We show that this problem is strongly NP-hard, and give an time dynamic programming algorithm and an time dynamic programming algorithm for the problem, where n is the number of jobs, m is the number of families, k is the number of distinct release dates and P is the sum of the setup times of all the families and the processing times of all the jobs. We further give a heuristic with a performance ratio 2. We also give a polynomial-time approximation scheme (PTAS) for the problem.     

One-dimensional and multi-dimensional substring selectivity estimation

With the increasing importance of XML, LDAP directories, and text-based information sources on the Internet, there is an ever-greater need to evaluate queries involving (sub)string matching. In many cases, matches need to be on multiple attributes/dimensions, with correlations between the multiple dimensions. Effective query optimization in this context requires good selectivity estimates. In this paper, we use pruned count-suffix trees (PSTs) as the basic data structure for substring selectivity estimation. For the 1-D problem, we present a novel technique called MO (Maximal Overlap). We then develop and analyze two 1-D estimation algorithms, MOC and MOLC, based on MO and a constraint-based characterization of all possible completions of a given PST. For the k-D problem, we first generalize PSTs to multiple dimensions and develop a space- and time-efficient probabilistic algorithm to construct k-D PSTs directly. We then show how to extend MO to multiple dimensions. Finally, we demonstrate, both analytically and experimentally, that MO is both practical and substantially superior to competing algorithms. Received April 28, 2000 / Accepted July 11, 2000     

Note: A method to isolate and detect a large number of single molecules by microdroplet fluorescence spectroscopy

A laser induced fluorescence system, in combination with a glass-frit nebulizer and a photo-voltaic cell detector, is described for single molecule detection. The glass-frit nebulizer continuously generates a large number of droplets with an average droplet size of three micrometers in diameter. Rhodamine 6G molecules were detected at the 10(-12) M level. Concentrations 10(-12)-10(-10) M would provide mostly single molecules (0, 1, 2, 3, ...) in the individual droplets, as determined by Poisson distribution.     

Can we teach digital natives digital literacy?

In recent years, there has been much debate about the concept of digital natives, in particular the differences between the digital natives' knowledge and adoption of digital technologies in informal versus formal educational contexts. This paper investigates the knowledge about educational technologies of a group of undergraduate students studying the course Introduction to eLearning at a university in Australia and how they adopt unfamiliar technologies into their learning. The study explores the 'digital nativeness' of these students by investigating their degree of digital literacy and the ease with which they learn to make use of unfamiliar technologies. The findings show that the undergraduates were generally able to use unfamiliar technologies easily in their learning to create useful artefacts. They need, however to be made aware of what constitutes educational technologies and be provided with the opportunity to use them for meaningful purposes. The self-perception measures of the study indicated that digital natives can be taught digital literacy.